Andrzej Majkowski

A new method of EEG classification for BCI with feature extraction based on higher order statistics of wavelet components and selection with genetic algorithms

A new method of feature extraction and selection of EEG signal for brain-computer interface design is presented. The proposed feature selection method is based on higher order statistics (HOS) calculated for the details of discrete wavelets transform (DWT) of EEG signal. Then a genetic algorithm is used for feature selection. During the experiment classification is conducted on a single trial of EEG signals. The proposed novel method of feature extraction using HOS and DWT gives more accurate results then the algorithm based on discrete Fourier transform (DFT).

A proposal of virtual laboratory structure

The nearly avalanche expansion of the information and communication technology (ICT) strongly influenced many domains of our lives. From the viewpoint of an academic teacher specialized in the area of instrumentation and measurement (I&M) these influences alter both measurement techniques and didactic process. Distance learning based on the Internet technology is becoming more and more popular. In this context remote virtual laboratories are very useful tools. Students can access virtual instruments via a computer network and carry out real experiments directly by using a standard Web browser. In the paper there is presented a proposal of a virtual laboratory structure. To the key elements of the proposed system architecture belong: the main server, the so called system manager and measurement server. The software is created for Microsoft Windows 2003 Server and Windows XP Professional. The system has a modular structure which can be scalable in a simple way

Robust PCA neural networks for random noise reduction of the data

The paper presents a principal component analysis (PCA) approach to the reduction of noise contaminating the data. The PCA performs the role of lossy compression and decompression. The compression/decompression provides the means of coding the data and then recovering it with some losses, dependent on the realized compression ratio. In this process some part of the information contained in the data is lost. When the loss tolerance is equal to the noise strength, the noise and the loss tolerance are augmented and the decompressed signal is deprived of noise. This way of noise filtering has been checked on examples of 1-dimensional and 2-dimensional data and the results of numerical experiments are included.

The next approach to the design of a Web-based virtual laboratory

Virtual instruments, as well as networked and distributed measurement systems, are the natural tools, which can be used in a modern didactic process for creating virtual laboratories offered by a group of Universities. In the paper a solution of the experimental model of a distributed measurement laboratory is presented.

Implementation of selected EEG signal processing algorithms in asynchronous BCI

This paper discusses an asynchronous system of brain-computer interface, operating in real time. In the proposed system, the processing, analysis and classification of EEG signal is implemented using the Matlab programming environment and the BCI2000 package.

A new method of spatial filters design for brain-computer interface based on steady state visually evoked potentials

In the article the authors present their own method of designing spatial filters to use in brain-computer interface, based on steady state visually evoked potentials. The spatial filter is calculated by minimizing a specially created objective function. The developed method allows us to create a dedicated filter for each user, however it demands a calibration session. By using designed spatial filters it is possible to identify visual potentials for very close frequencies of flickering light with good efficiency (information transfer rate at 27-57bit/min).

Emotion recognition using facial expressions

Abstract In the article there are presented the results of recognition of seven emotional states (neutral, joy, sadness, surprise, anger, fear, disgust) based on facial expressions. Coefficients describing elements of facial expressions, registered for six subjects, were used as features. The features have been calculated for three-dimensional face model. The classification of features were performed using k-NN classifier and MLP neural network.

Implementation of automatic feature selection methods for BCI realization

The main task of brain-computer interface is to translate signals generated by neurons of the brain into commands. For the effective operation of BCI, efficient methods of feature selection of EEG signal are needed. In this article authors propose the use of correlation and t-statistics to feature selection.

System for automatic heart rate calculation in epileptic seizures

This article presents a comprehensive system for automatic heart rate (HR) detection. The system is robust and resistant to disturbances (noise, interferences, artifacts) occurring mainly during epileptic seizures. ECG signal filtration (IIR) and normalization due to skewness and standard deviation were used as preprocessing steps. A key element of the system is a reference QRS complex pattern calculated individually for each ECG recording. Next, a cross-correlation of the reference QRS pattern with short, normalized ECG windows is calculated and the maxima of the correlation are found (R-wave locations). Determination of the RR intervals makes possible calculation of heart rate changes and also heart rate variability (HRV). The algorithm was tested using a simulation in which a noise of an amplitude several times higher than ECG standard deviation levels was added. The proposed algorithm is characterized by high QRS detection accuracy, and high sensitivity and specificity. The algorithm proved to be useful in clinical practice, where it was used to automatically determine HR for ECG signals recorded before and during 58 focal seizures in 56 adult patients with intractable temporal lobe epilepsy.

Selection of EEG signal features for ERD/ERS classification using genetic algorithms

The article presents the use of genetic algorithm (GA) to select and classify ERD/ERS patterns. One hundred twenty eight channel EEG signal was used in the experiments. The signal was recorded for 40 people, during the process of imagining right and left hand movements. Feature extraction was performed using frequency analysis (FFT) with the resolution of 1Hz. So the features were spectral lines associated with particular electrodes. The selection of features, calculated for all people, was made with GA. The fitness function used in GA was EEG signal classification error calculated using LDA classifier and 5-CV test. The average accuracy of the classification for all people in 8–30Hz band was 0.85, while for the top 10 results 0.92.